Appreciative inquiry: a strength-based research approach to building Canadian public health nursing capacity
Bibliographic record
Abstract
In this paper we evaluate the use of appreciative inquiry in focus groups with public health nurses, managers and policy makers across Canada as part of our project to generate policy recommendations for building public health nursing capacity. The focus group protocol successfully involved participants in data collection and analysis through a unique combination of appreciative inquiry and nominal group process. This approach resulted in credible data for analysis, and the final analysis met scientific research standards. The evaluation revealed that our process was effective in engaging participants when their time available was limited, no matter what their position or public health setting, and in eliciting solution-focused results. By focusing on what works well in an organisation, appreciative inquiry enabled us to identify the positive attributes of organisations that best support public health nursing practice and to develop practical policy recommendations because they were based on participants’ experience. Further, appreciative inquiry was especially effective with public health policy makers and nurses as it is consistent with the strength-based, capacity building approaches inherent in public health nursing practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.119 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.023 | 0.043 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".